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  <div class="section" id="mindspore-ops-cropandresize">
<h1>mindspore.ops.CropAndResize<a class="headerlink" href="#mindspore-ops-cropandresize" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.ops.CropAndResize">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.ops.</code><code class="sig-name descname">CropAndResize</code><span class="sig-paren">(</span><em class="sig-param">method=&quot;bilinear&quot;</em>, <em class="sig-param">extrapolation_value=0.0</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mindspore/ops/operations/image_ops.html#CropAndResize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mindspore.ops.CropAndResize" title="Permalink to this definition">¶</a></dt>
<dd><p>Extracts crops from the input image tensor and resizes them.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In case that the output shape depends on crop_size, the crop_size must be constant.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>method</strong> (<a class="reference external" href="https://docs.python.org/library/stdtypes.html#str" title="(in Python v3.8)"><em>str</em></a>) – An optional string that specifies the sampling method for resizing.
It can be “bilinear”, “nearest” or “bilinear_v2”. The option “bilinear” stands for standard bilinear
interpolation algorithm, while “bilinear_v2” may result in better result in some cases. Default: “bilinear”</p></li>
<li><p><strong>extrapolation_value</strong> (<a class="reference external" href="https://docs.python.org/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a>) – An optional float value used extrapolation, if applicable. Default: 0.0.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Inputs:</dt><dd><ul class="simple">
<li><p><strong>x</strong> (Tensor) - The input image must be a 4-D tensor of shape [batch, image_height, image_width, depth].
Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16.</p></li>
<li><p><strong>boxes</strong> (Tensor) - A 2-D tensor of shape [num_boxes, 4].
The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image
and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to
the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is
mapped to [0, image_height - 1] in image height coordinates. We do allow y1 &gt; y2, in which case the sampled
crop is an up-down flipped version of the original image. The width dimension is treated similarly.
Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to
extrapolate the input image values. Types allowed: float32.</p></li>
<li><p><strong>box_index</strong> (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
The value of box_ind[i] specifies the image that the i-th box refers to. Types allowed: int32.</p></li>
<li><p><strong>crop_size</strong> (Tuple[int]) - A tuple of two int32 elements: (crop_height, crop_width).
Only constant value is allowed. All cropped image patches are resized to this size.
The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.</p></li>
</ul>
</dd>
<dt>Outputs:</dt><dd><p>A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Raises</dt>
<dd class="field-odd"><ul class="simple">
<li><p><a class="reference external" href="https://docs.python.org/library/exceptions.html#TypeError" title="(in Python v3.8)"><strong>TypeError</strong></a> – If <cite>method</cite> is not a str.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/library/exceptions.html#TypeError" title="(in Python v3.8)"><strong>TypeError</strong></a> – If <cite>extrapolation_value</cite> is not a float.</p></li>
<li><p><a class="reference external" href="https://docs.python.org/library/exceptions.html#ValueError" title="(in Python v3.8)"><strong>ValueError</strong></a> – If <cite>method</cite> is not one of ‘bilinear’, ‘nearest’, ‘bilinear_v2’.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Supported Platforms:</dt><dd><p><code class="docutils literal notranslate"><span class="pre">Ascend</span></code> <code class="docutils literal notranslate"><span class="pre">GPU</span></code> <code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">CropAndResizeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Cell</span><span class="p">):</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">crop_size</span><span class="p">):</span>
<span class="gp">... </span>        <span class="nb">super</span><span class="p">(</span><span class="n">CropAndResizeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">crop_and_resize</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">CropAndResize</span><span class="p">()</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">crop_size</span> <span class="o">=</span> <span class="n">crop_size</span>
<span class="gp">...</span>
<span class="gp">... </span>    <span class="k">def</span> <span class="nf">construct</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">boxes</span><span class="p">,</span> <span class="n">box_index</span><span class="p">):</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">crop_and_resize</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">boxes</span><span class="p">,</span> <span class="n">box_index</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">crop_size</span><span class="p">)</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">NUM_BOXES</span> <span class="o">=</span> <span class="mi">5</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">IMAGE_HEIGHT</span> <span class="o">=</span> <span class="mi">256</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">IMAGE_WIDTH</span> <span class="o">=</span> <span class="mi">256</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">CHANNELS</span> <span class="o">=</span> <span class="mi">3</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="n">IMAGE_HEIGHT</span><span class="p">,</span> <span class="n">IMAGE_WIDTH</span><span class="p">,</span> <span class="n">CHANNELS</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">boxes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">NUM_BOXES</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">box_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="n">NUM_BOXES</span><span class="p">],</span> <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">crop_size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">24</span><span class="p">,</span> <span class="mi">24</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">crop_and_resize</span> <span class="o">=</span> <span class="n">CropAndResizeNet</span><span class="p">(</span><span class="n">crop_size</span><span class="o">=</span><span class="n">crop_size</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">crop_and_resize</span><span class="p">(</span><span class="n">Tensor</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">boxes</span><span class="p">),</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">box_index</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(5, 24, 24, 3)</span>
</pre></div>
</div>
</dd></dl>

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